Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model
Open accessAbstract
This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy.
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Previous health economic studies in China have only compared a specific AI-based screening strategy with conventional manual screening.
“Previous health economic studies in China have only compared a specific AI-based screening strategy with conventional manual screening, to indicate the cost-effectiveness of using AI for screening DR14,15,37–39.”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [38]
The Materials and methods section describes a specific AI screening system, EyeWisdom, and an ophthalmologist-led screening mode. But the Discussion also compares AI screening with no screening, so this paper does not support the claim that the comparison was only with conventional manual screening. It also cannot establish what all previous Chinese studies compared. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMost previous health economic studies in China leaned towards adopting AI models with a high specificity (over 97%).
“Most of these studies leaned towards adopting AI models with a high specificity (over 97%).”
From Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case (2024) · cites this paper as [38]
The Introduction says Chinese economic evaluations of AI-based diabetic-retinopathy screening are limited, but does not describe the specificity adopted by most previous studies. The Discussion mentions 97% sensitivity and 87% specificity, not a survey of prior economic studies using specificity over 97%. Searched the paper for specificity figures and prior-study comparisons. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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Global Burden of Diabetes and Cost-Effectiveness of Diabetes Technologies
Saboo, B., & Ghosh, S. (2026) · Springer eBooks · doi:10.1007/978-3-032-24854-1_2
- Authors
- Li, H., Li, G., Li, N., Liu, C., Yuan, Z., Gao, Q., Hao, S., Fan, S., & Yang, J.
- Affiliations
- Changzhi Medical College (CN)
- Corresponding author
- Hao, Shaofeng
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2023-11-16
- Volume
- 18
- Issue
- 11
- Pages
- e0291390
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC10653408
- OpenAlex
- W4388728436
- Cited by
- 27
- References
- 59
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 5 · 2023: 1
- FWCI
- 4.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- General Project of Shanxi Provincial Key R & D Plan (social development field): Research on Screening Model of Diabetic Retinopathy Based on Artificial Intelligence Diagnosis System (201903D321031)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Keywords
- retinopathy, cost-effectiveness, rural China, diabetes mellitus, quality-adjusted life years, incremental cost-effectiveness ratio, Markov model, health economic evaluation, screening programs, sensitivity analysis
- MeSH terms
- Cost-Effectiveness Analysis, Artificial Intelligence, China, Cost-Benefit Analysis, Diabetes Mellitus, Diabetic Retinopathy, Humans, Markov Chains, Mass Screening, Quality of Life, Rural Population, Quality-Adjusted Life Years
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